Added Float type to the function parameter values (#77)

demos/employee_details_copilot/Bolt-FC-1B-Q3_K_L.model_file

@@ -1,25 +0,0 @@
-FROM Bolt-Function-Calling-1B-Q3_K_L.gguf
-
-# Set the size of the context window used to generate the next token
-# PARAMETER num_ctx 16384
-PARAMETER num_ctx 4096
-
-# Set parameters for response generation
-PARAMETER num_predict 1024
-PARAMETER temperature 0.1
-PARAMETER top_p 0.5
-PARAMETER top_k 32022
-PARAMETER repeat_penalty 1.0
-PARAMETER stop "<|EOT|>"
-
-# Set the random number seed to use for generation
-PARAMETER seed 42
-
-# Set the prompt template to be passed into the model
-TEMPLATE """{{ if .System }}<｜begin▁of▁sentence｜>
-{{ .System }}
-{{ end }}{{ if .Prompt }}### Instruction:
-{{ .Prompt }}
-{{ end }}### Response:
-{{ .Response }}
-<|EOT|>"""

demos/employee_details_copilot/README.md

@@ -2,10 +2,6 @@
 This demo shows how you can use intelligent prompt gateway to act a copilot for calling the correct proc by capturing the required and optional parametrs from the prompt. This demo assumes you are using ollama running natively. If you want to run ollama running inside docker then please update ollama endpoint in docker-compose file.
 
 # Starting the demo
-1. Ensure that submodule is up to date
-   ```sh
-   git submodule sync --recursive
-   ```
 1. Create `.env` file and set OpenAI key using env var `OPENAI_API_KEY`
 1. Start services
    ```sh

demos/employee_details_copilot/api_server/app/functions.py

@@ -0,0 +1,29 @@
+from typing import List, Optional
+
+# Function for top_employees
+def top_employees(grouping: str, ranking_criteria: str, top_n: int):
+    pass
+
+# Function for aggregate_stats
+def aggregate_stats(grouping: str, aggregate_criteria: str, aggregate_type: str):
+    pass
+
+# Function for employees_projects
+def employees_projects(min_performance_score: float, min_years_experience: int, department: str, min_project_count: int = None, months_range: int = None):
+    pass
+
+# Function for salary_growth
+def salary_growth(min_salary_increase_percentage: float, department: str = None):
+    pass
+
+# Function for promotions_increases
+def promotions_increases(year: int, min_salary_increase_percentage: float = None, department: str = None):
+    pass
+
+# Function for avg_project_performance
+def avg_project_performance(min_project_count: int, min_performance_score: float, department: str = None):
+    pass
+
+# Function for certifications_experience
+def certifications_experience(certifications: list, min_years_experience: int, department: str = None):
+    pass

demos/employee_details_copilot/api_server/app/generate_config.py

@@ -0,0 +1,78 @@
+import inspect
+import yaml
+import functions  # This is your module containing the function definitions
+import os
+
+
+def generate_config_from_function(func):
+    func_name = func.__name__
+    func_doc = func.__doc__
+
+    # Get function signature
+    sig = inspect.signature(func)
+    params = []
+
+    # Extract parameter info
+    for name, param in sig.parameters.items():
+        param_info = {
+            'name': name,
+            'description': f"Provide the {name.replace('_', ' ')}",  # Customize as needed
+            'required': param.default == inspect.Parameter.empty,  # True if no default value
+            'type': param.annotation.__name__ if param.annotation != inspect.Parameter.empty else 'str'  # Get type if available
+        }
+        params.append(param_info)
+
+    # Define the config for this function
+    config = {
+        'name': func_name,
+        'description': func_doc or "",
+        'parameters': params,
+        'endpoint': {
+            'cluster': 'api_server',
+            'path': f"/{func_name}"
+        },
+        'system_prompt': f"You are responsible for handling {func_name} requests."
+    }
+
+    return config
+
+
+def generate_full_config(module):
+    config = {'prompt_targets': []}
+
+    # Automatically get all functions from the module
+    functions_list = inspect.getmembers(module, inspect.isfunction)
+
+    for func_name, func_obj in functions_list:
+        func_config = generate_config_from_function(func_obj)
+        config['prompt_targets'].append(func_config)
+
+    return config
+
+
+def replace_prompt_targets_in_config(file_path, new_prompt_targets):
+    # Load the existing bolt_config.yaml
+    with open(file_path, 'r') as file:
+        config_data = yaml.safe_load(file)
+
+    # Replace the prompt_targets section with the new one
+    config_data['prompt_targets'] = new_prompt_targets
+
+    # Write the updated config back to the YAML file
+    with open("bolt_config.yaml", 'w+') as file:
+        yaml.dump(config_data, file, sort_keys=False)
+
+    print(f"Updated prompt_targets in bolt_config.yaml")
+
+
+# Main execution
+if __name__ == "__main__":
+    # Path to the existing bolt_config.yaml two directories up
+    bolt_config_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../bolt_config.yaml'))
+
+    # Generate new prompt_targets from the functions module
+    new_config = generate_full_config(functions)
+    new_prompt_targets = new_config['prompt_targets']
+
+    # Replace the prompt_targets in the existing bolt_config.yaml
+    replace_prompt_targets_in_config(bolt_config_path, new_prompt_targets)

demos/employee_details_copilot/api_server/app/main.py

@@ -1,7 +1,6 @@
 import random
 from typing import List
 from fastapi import FastAPI, HTTPException, Response
-from datetime import datetime, date, timedelta, timezone
 import logging
 from pydantic import BaseModel
 from utils import load_sql
@@ -118,7 +117,7 @@ class TopEmployeesProjects(BaseModel):
     months_range: int = None  # Optional (for filtering recent projects)
 
 
-@app.post("/top_employees_projects")
+@app.post("/employees_projects")
 async def employees_projects(req: TopEmployeesProjects, res: Response):
     params, filters = {}, []
 
@@ -225,8 +224,8 @@ class AvgProjPerformanceRequest(BaseModel):
     department: str = None  # Optional
 
 
-@app.post("/avg_project_performance")
-async def avg_project_performance(req: AvgProjPerformanceRequest, res: Response):
+@app.post("/project_performance")
+async def project_performance(req: AvgProjPerformanceRequest, res: Response):
     params, filters = {}, []
 
     if req.department:
@@ -257,7 +256,7 @@ class CertificationsExperienceRequest(BaseModel):
     min_years_experience: int
     department: str = None  # Optional
 
-@app.post("/employees_certifications_experience")
+@app.post("/certifications_experience")
 async def certifications_experience(req: CertificationsExperienceRequest, res: Response):
     # Convert the list of certifications into a format for SQL query
     certs_filter = ', '.join([f"'{cert}'" for cert in req.certifications])

demos/employee_details_copilot/bolt_config.yaml

@@ -149,7 +149,7 @@ prompt_targets:
 
   # 5. Employees with Highest Average Project Performance
   - type: function_resolver
-    name: avg_project_performance
+    name: project_performance
     description: |
       Fetch employees with the highest average performance across all projects they have worked on over time. You can filter by minimum project count, department, and minimum performance score.
     parameters:
@@ -161,13 +161,14 @@ prompt_targets:
         description: Minimum performance score to filter employees.
         required: true
         type: float
+        default: 4.0
       - name: department
         description: Department to filter by (optional).
         required: false
         type: string
     endpoint:
       cluster: api_server
-      path: /avg_project_performance
+      path: /project_performance
     system_prompt: |
       You are responsible for fetching employees with the highest average performance across all projects they’ve worked on. Apply filters for minimum project count, performance score, and department.
 
@@ -190,6 +191,7 @@ prompt_targets:
         description: Department to filter employees by (optional).
         required: false
         type: string
+        default: "Engineering"
     endpoint:
       cluster: api_server
       path: /certifications_experience